Attributeerror: ‘columntransformer’ object has no attribute ‘get_feature_names’

The error message “AttributeError: ‘ColumnTransformer’ object has no attribute ‘get_feature_names'” occurs when you try to use the `get_feature_names` method on a `ColumnTransformer` object, but this method is not available in this class. The `get_feature_names` method is typically used to retrieve the feature names from a feature transformation pipeline.

The `ColumnTransformer` class in scikit-learn allows you to apply different transformations to different columns of your input data. However, it does not provide a built-in method to get the transformed feature names. This is because the feature names might differ for each transformer, making it ambiguous to retrieve a consistent set of names.

To work around this issue, you can manually extract and combine the feature names from the individual transformers in the `ColumnTransformer`.

Here is an example to illustrate how you can obtain the feature names using a `ColumnTransformer`:

    from sklearn.compose import ColumnTransformer
    from sklearn.preprocessing import StandardScaler, OneHotEncoder
    from sklearn.pipeline import Pipeline

    # Define the transformers
    numeric_transformer = Pipeline(steps=[('scaler', StandardScaler())])
    categorical_transformer = Pipeline(steps=[('encoder', OneHotEncoder())])

    # Define the column transformer
    preprocessor = ColumnTransformer(transformers=[
        ('num', numeric_transformer, ['numerical_column']),
        ('cat', categorical_transformer, ['categorical_column'])
    ])

    # Fit the column transformer on the training data
    preprocessor.fit(X_train)

    # Get the transformed feature names
    feature_names = list(preprocessor.named_transformers_['num']['scaler'].get_feature_names(['numerical_column']))
    feature_names += list(preprocessor.named_transformers_['cat']['encoder'].get_feature_names(['categorical_column']))
    print(feature_names)
  

In the above example, we first define two transformers: `numeric_transformer` to scale the numerical column, and `categorical_transformer` to encode the categorical column. We then define the `ColumnTransformer` where we specify the transformers and the corresponding column names.

After fitting the `ColumnTransformer` on our training data `X_train`, we obtain the transformed feature names by retrieving the individual transformers from the `named_transformers_` attribute and calling the `get_feature_names` method on each transformer with the respective column names. We concatenate these feature names into a single list.

Finally, we print the `feature_names` list which contains the transformed feature names.